Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations2930
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows3
Duplicate rows (%)0.1%
Total size in memory375.0 KiB
Average record size in memory131.0 B

Variable types

Numeric8
Categorical2

Alerts

Dataset has 3 (0.1%) duplicate rowsDuplicates
Bedroom AbvGr is highly overall correlated with Gr Liv AreaHigh correlation
Garage Cars is highly overall correlated with Gr Liv Area and 3 other fieldsHigh correlation
Gr Liv Area is highly overall correlated with Bedroom AbvGr and 3 other fieldsHigh correlation
Overall Qual is highly overall correlated with Garage Cars and 3 other fieldsHigh correlation
SalePrice is highly overall correlated with Garage Cars and 4 other fieldsHigh correlation
Total Bsmt SF is highly overall correlated with SalePriceHigh correlation
Year Built is highly overall correlated with Garage Cars and 2 other fieldsHigh correlation
Garage Cars has 157 (5.4%) zeros Zeros
Total Bsmt SF has 79 (2.7%) zeros Zeros

Reproduction

Analysis started2025-05-05 15:26:55.465095
Analysis finished2025-05-05 15:27:02.526347
Duration7.06 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Lot Area
Real number (ℝ)

Distinct1960
Distinct (%)66.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10147.922
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-05-06T00:27:02.599350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3188.3
Q17440.25
median9436.5
Q311555.25
95-th percentile17131
Maximum215245
Range213945
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation7880.0178
Coefficient of variation (CV)0.77651542
Kurtosis265.02367
Mean10147.922
Median Absolute Deviation (MAD)2040
Skewness12.820898
Sum29733411
Variance62094680
MonotonicityNot monotonic
2025-05-06T00:27:02.717347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9600 44
 
1.5%
7200 43
 
1.5%
6000 34
 
1.2%
9000 29
 
1.0%
10800 25
 
0.9%
8400 21
 
0.7%
7500 21
 
0.7%
6240 18
 
0.6%
1680 18
 
0.6%
6120 17
 
0.6%
Other values (1950) 2660
90.8%
ValueCountFrequency (%)
1300 1
< 0.1%
1470 1
< 0.1%
1476 1
< 0.1%
1477 2
0.1%
1484 1
< 0.1%
1488 1
< 0.1%
1491 1
< 0.1%
1495 1
< 0.1%
1504 1
< 0.1%
1526 2
0.1%
ValueCountFrequency (%)
215245 1
< 0.1%
164660 1
< 0.1%
159000 1
< 0.1%
115149 1
< 0.1%
70761 1
< 0.1%
63887 1
< 0.1%
57200 1
< 0.1%
56600 1
< 0.1%
53504 1
< 0.1%
53227 1
< 0.1%

Overall Qual
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0948805
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-05-06T00:27:02.799324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4110261
Coefficient of variation (CV)0.23151005
Kurtosis0.05241245
Mean6.0948805
Median Absolute Deviation (MAD)1
Skewness0.19063396
Sum17858
Variance1.9909946
MonotonicityNot monotonic
2025-05-06T00:27:02.868300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 825
28.2%
6 732
25.0%
7 602
20.5%
8 350
11.9%
4 226
 
7.7%
9 107
 
3.7%
3 40
 
1.4%
10 31
 
1.1%
2 13
 
0.4%
1 4
 
0.1%
ValueCountFrequency (%)
1 4
 
0.1%
2 13
 
0.4%
3 40
 
1.4%
4 226
 
7.7%
5 825
28.2%
6 732
25.0%
7 602
20.5%
8 350
11.9%
9 107
 
3.7%
10 31
 
1.1%
ValueCountFrequency (%)
10 31
 
1.1%
9 107
 
3.7%
8 350
11.9%
7 602
20.5%
6 732
25.0%
5 825
28.2%
4 226
 
7.7%
3 40
 
1.4%
2 13
 
0.4%
1 4
 
0.1%

Year Built
Real number (ℝ)

High correlation 

Distinct118
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.3563
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-05-06T00:27:02.956303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1915
Q11954
median1973
Q32001
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)47

Descriptive statistics

Standard deviation30.245361
Coefficient of variation (CV)0.015342412
Kurtosis-0.50171504
Mean1971.3563
Median Absolute Deviation (MAD)25
Skewness-0.60446222
Sum5776074
Variance914.78184
MonotonicityNot monotonic
2025-05-06T00:27:03.063303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 142
 
4.8%
2006 138
 
4.7%
2007 109
 
3.7%
2004 99
 
3.4%
2003 88
 
3.0%
1977 57
 
1.9%
1920 57
 
1.9%
1976 54
 
1.8%
1999 52
 
1.8%
2008 49
 
1.7%
Other values (108) 2085
71.2%
ValueCountFrequency (%)
1872 1
 
< 0.1%
1875 1
 
< 0.1%
1879 1
 
< 0.1%
1880 5
0.2%
1882 1
 
< 0.1%
1885 2
 
0.1%
1890 7
0.2%
1892 2
 
0.1%
1893 1
 
< 0.1%
1895 3
0.1%
ValueCountFrequency (%)
2010 3
 
0.1%
2009 25
 
0.9%
2008 49
 
1.7%
2007 109
3.7%
2006 138
4.7%
2005 142
4.8%
2004 99
3.4%
2003 88
3.0%
2002 47
 
1.6%
2001 35
 
1.2%

Gr Liv Area
Real number (ℝ)

High correlation 

Distinct1292
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1499.6904
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-05-06T00:27:03.173303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile861
Q11126
median1442
Q31742.75
95-th percentile2463.1
Maximum5642
Range5308
Interquartile range (IQR)616.75

Descriptive statistics

Standard deviation505.50889
Coefficient of variation (CV)0.33707549
Kurtosis4.1378382
Mean1499.6904
Median Absolute Deviation (MAD)311
Skewness1.2741097
Sum4394093
Variance255539.24
MonotonicityNot monotonic
2025-05-06T00:27:03.283324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 41
 
1.4%
1092 26
 
0.9%
1040 25
 
0.9%
1456 20
 
0.7%
1200 18
 
0.6%
894 15
 
0.5%
912 14
 
0.5%
816 14
 
0.5%
1728 13
 
0.4%
848 13
 
0.4%
Other values (1282) 2731
93.2%
ValueCountFrequency (%)
334 1
< 0.1%
407 1
< 0.1%
438 1
< 0.1%
480 1
< 0.1%
492 1
< 0.1%
498 1
< 0.1%
520 1
< 0.1%
540 1
< 0.1%
572 1
< 0.1%
599 1
< 0.1%
ValueCountFrequency (%)
5642 1
< 0.1%
5095 1
< 0.1%
4676 1
< 0.1%
4476 1
< 0.1%
4316 1
< 0.1%
3820 1
< 0.1%
3672 1
< 0.1%
3627 1
< 0.1%
3608 1
< 0.1%
3500 1
< 0.1%

Garage Cars
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.7668146
Minimum0
Maximum5
Zeros157
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-05-06T00:27:03.362335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.76056636
Coefficient of variation (CV)0.43047321
Kurtosis0.24496945
Mean1.7668146
Median Absolute Deviation (MAD)0
Skewness-0.21983636
Sum5175
Variance0.5784612
MonotonicityNot monotonic
2025-05-06T00:27:03.429324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 1603
54.7%
1 778
26.6%
3 374
 
12.8%
0 157
 
5.4%
4 16
 
0.5%
5 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 157
 
5.4%
1 778
26.6%
2 1603
54.7%
3 374
 
12.8%
4 16
 
0.5%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 16
 
0.5%
3 374
 
12.8%
2 1603
54.7%
1 778
26.6%
0 157
 
5.4%

Total Bsmt SF
Real number (ℝ)

High correlation  Zeros 

Distinct1058
Distinct (%)36.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1051.6145
Minimum0
Maximum6110
Zeros79
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-05-06T00:27:03.519335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile453
Q1793
median990
Q31302
95-th percentile1776
Maximum6110
Range6110
Interquartile range (IQR)509

Descriptive statistics

Standard deviation440.61507
Coefficient of variation (CV)0.41898913
Kurtosis9.1356123
Mean1051.6145
Median Absolute Deviation (MAD)236
Skewness1.1562043
Sum3080179
Variance194141.64
MonotonicityNot monotonic
2025-05-06T00:27:03.626335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79
 
2.7%
864 74
 
2.5%
672 29
 
1.0%
912 26
 
0.9%
1040 25
 
0.9%
768 24
 
0.8%
816 23
 
0.8%
728 21
 
0.7%
384 19
 
0.6%
1008 19
 
0.6%
Other values (1048) 2590
88.4%
ValueCountFrequency (%)
0 79
2.7%
105 1
 
< 0.1%
160 1
 
< 0.1%
173 1
 
< 0.1%
190 1
 
< 0.1%
192 1
 
< 0.1%
216 2
 
0.1%
240 1
 
< 0.1%
245 1
 
< 0.1%
264 4
 
0.1%
ValueCountFrequency (%)
6110 1
< 0.1%
5095 1
< 0.1%
3206 1
< 0.1%
3200 1
< 0.1%
3138 1
< 0.1%
3094 1
< 0.1%
2846 1
< 0.1%
2660 1
< 0.1%
2633 1
< 0.1%
2630 1
< 0.1%

Full Bath
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
2
1532 
1
1318 
3
 
64
0
 
12
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2930
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 1532
52.3%
1 1318
45.0%
3 64
 
2.2%
0 12
 
0.4%
4 4
 
0.1%

Length

2025-05-06T00:27:03.728315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T00:27:03.792313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 1532
52.3%
1 1318
45.0%
3 64
 
2.2%
0 12
 
0.4%
4 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 1532
52.3%
1 1318
45.0%
3 64
 
2.2%
0 12
 
0.4%
4 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1532
52.3%
1 1318
45.0%
3 64
 
2.2%
0 12
 
0.4%
4 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1532
52.3%
1 1318
45.0%
3 64
 
2.2%
0 12
 
0.4%
4 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1532
52.3%
1 1318
45.0%
3 64
 
2.2%
0 12
 
0.4%
4 4
 
0.1%

Bedroom AbvGr
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8542662
Minimum0
Maximum8
Zeros8
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-05-06T00:27:03.855325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.82773114
Coefficient of variation (CV)0.28999788
Kurtosis1.8914207
Mean2.8542662
Median Absolute Deviation (MAD)0
Skewness0.30569421
Sum8363
Variance0.68513884
MonotonicityNot monotonic
2025-05-06T00:27:03.927324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 1597
54.5%
2 743
25.4%
4 400
 
13.7%
1 112
 
3.8%
5 48
 
1.6%
6 21
 
0.7%
0 8
 
0.3%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 8
 
0.3%
1 112
 
3.8%
2 743
25.4%
3 1597
54.5%
4 400
 
13.7%
5 48
 
1.6%
6 21
 
0.7%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 21
 
0.7%
5 48
 
1.6%
4 400
 
13.7%
3 1597
54.5%
2 743
25.4%
1 112
 
3.8%
0 8
 
0.3%

Kitchen Qual
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size168.9 KiB
TA
1494 
Gd
1160 
Ex
205 
Fa
 
70
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5860
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTA
2nd rowTA
3rd rowGd
4th rowEx
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1494
51.0%
Gd 1160
39.6%
Ex 205
 
7.0%
Fa 70
 
2.4%
Po 1
 
< 0.1%

Length

2025-05-06T00:27:04.009335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T00:27:04.069974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ta 1494
51.0%
gd 1160
39.6%
ex 205
 
7.0%
fa 70
 
2.4%
po 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 1494
25.5%
A 1494
25.5%
G 1160
19.8%
d 1160
19.8%
E 205
 
3.5%
x 205
 
3.5%
F 70
 
1.2%
a 70
 
1.2%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1494
25.5%
A 1494
25.5%
G 1160
19.8%
d 1160
19.8%
E 205
 
3.5%
x 205
 
3.5%
F 70
 
1.2%
a 70
 
1.2%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1494
25.5%
A 1494
25.5%
G 1160
19.8%
d 1160
19.8%
E 205
 
3.5%
x 205
 
3.5%
F 70
 
1.2%
a 70
 
1.2%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1494
25.5%
A 1494
25.5%
G 1160
19.8%
d 1160
19.8%
E 205
 
3.5%
x 205
 
3.5%
F 70
 
1.2%
a 70
 
1.2%
P 1
 
< 0.1%
o 1
 
< 0.1%

SalePrice
Real number (ℝ)

High correlation 

Distinct1032
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180796.06
Minimum12789
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-05-06T00:27:04.388000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12789
5-th percentile87500
Q1129500
median160000
Q3213500
95-th percentile335000
Maximum755000
Range742211
Interquartile range (IQR)84000

Descriptive statistics

Standard deviation79886.692
Coefficient of variation (CV)0.4418608
Kurtosis5.1189
Mean180796.06
Median Absolute Deviation (MAD)37000
Skewness1.7435001
Sum5.2973246 × 108
Variance6.3818836 × 109
MonotonicityNot monotonic
2025-05-06T00:27:04.497000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135000 34
 
1.2%
140000 33
 
1.1%
130000 29
 
1.0%
155000 28
 
1.0%
145000 26
 
0.9%
160000 23
 
0.8%
110000 21
 
0.7%
185000 21
 
0.7%
127000 20
 
0.7%
120000 20
 
0.7%
Other values (1022) 2675
91.3%
ValueCountFrequency (%)
12789 1
< 0.1%
13100 1
< 0.1%
34900 1
< 0.1%
35000 1
< 0.1%
35311 1
< 0.1%
37900 1
< 0.1%
39300 1
< 0.1%
40000 1
< 0.1%
44000 1
< 0.1%
45000 1
< 0.1%
ValueCountFrequency (%)
755000 1
< 0.1%
745000 1
< 0.1%
625000 1
< 0.1%
615000 1
< 0.1%
611657 1
< 0.1%
610000 1
< 0.1%
591587 1
< 0.1%
584500 1
< 0.1%
582933 1
< 0.1%
556581 1
< 0.1%

Interactions

2025-05-06T00:27:01.499363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T00:26:56.080856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T00:26:56.767412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T00:26:57.436901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T00:26:58.156961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T00:26:58.832928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T00:26:59.517625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T00:27:00.230637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T00:27:01.587356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-06T00:27:01.397354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

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Bedroom AbvGrFull BathGarage CarsGr Liv AreaKitchen QualLot AreaOverall QualSalePriceTotal Bsmt SFYear Built
Bedroom AbvGr1.0000.4350.1220.5260.0930.2990.0780.1970.056-0.032
Full Bath0.4351.0000.3400.3850.2330.0590.3060.3520.1980.304
Garage Cars0.1220.3401.0000.5240.3050.3440.6110.7020.4500.601
Gr Liv Area0.5260.3850.5241.0000.2270.4180.5780.7230.3790.317
Kitchen Qual0.0930.2330.3050.2271.0000.0000.4730.4230.2510.343
Lot Area0.2990.0590.3440.4180.0001.0000.1970.4290.3530.121
Overall Qual0.0780.3060.6110.5780.4730.1971.0000.8090.4730.665
SalePrice0.1970.3520.7020.7230.4230.4290.8091.0000.6060.681
Total Bsmt SF0.0560.1980.4500.3790.2510.3530.4730.6061.0000.442
Year Built-0.0320.3040.6010.3170.3430.1210.6650.6810.4421.000

Missing values

2025-05-06T00:27:02.274382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-06T00:27:02.375372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-06T00:27:02.477353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Lot AreaOverall QualYear BuiltGr Liv AreaGarage CarsTotal Bsmt SFFull BathBedroom AbvGrKitchen QualSalePrice
0317706196016562.01080.013TA215000
111622519618961.0882.012TA105000
2142676195813291.01329.013Gd172000
3111607196821102.02110.023Ex244000
4138305199716292.0928.023TA189900
599786199816042.0926.023Gd195500
649208200113382.01338.022Gd213500
750058199212802.01280.022Gd191500
853898199516162.01595.022Gd236500
975007199918042.0994.023Gd189000
Lot AreaOverall QualYear BuiltGr Liv AreaGarage CarsTotal Bsmt SFFull BathBedroom AbvGrKitchen QualSalePrice
292018944197010921.0546.013TA71000
2921126406197617282.01728.024TA150900
292292975197617282.01728.024TA188000
2923174005197711262.01126.023TA160000
2924200005196012242.01224.014TA131000
292579376198410032.01003.013TA142500
29268885519839022.0864.012TA131000
292710441519929700.0912.013TA132000
2928100105197413892.01389.012TA170000
292996277199320003.0996.023TA188000

Duplicate rows

Most frequently occurring

Lot AreaOverall QualYear BuiltGr Liv AreaGarage CarsTotal Bsmt SFFull BathBedroom AbvGrKitchen QualSalePrice# duplicates
045908200615542.01554.022Gd2095002
170185197915352.00.024TA1188582
2108005198712000.01200.033TA1790002